Semantic Segmentation of Color Eye Images for Improving Iris Segmentation

Osorio-Roig, Daile; Morales-González, Annette; Garea-Llano, Eduardo

doi:10.1007/978-3-319-75193-1_56

Cited by 7 publications

(5 citation statements)

References 16 publications

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“…Wang et al [37] proposed a lightweight network for iris semantic segmentation. Osorio et al [38], [39] developed a multi-class segmentation network for VS images. Li et al [40] developed a segmentation network that works in uncooperative scenarios.…”

Section: B Iris Semantic Segmentationmentioning

confidence: 99%

Toward an Efficient Iris Recognition System on Embedded Devices

Benalcazar,

Tapia,

Vasquez

et al. 2023

IEEE Access

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Iris Recognition (IR) is one of the market's most reliable and accurate biometric systems. Today, it is challenging to build NIR-capturing devices under the premise of hardware price reduction. Commercial NIR sensors are protected from modification. The process of building a new device is not trivial because it is required to start from scratch with the process of capturing images with quality, calibrating operational distances, and building lightweight software such as eyes/iris detectors and segmentation subsystems. In light of such challenges, this work aims to develop and implement iris recognition software in an embedding system and calibrate NIR in a contactless binocular setup. We evaluate and contrast speed versus performance obtained with two embedded computers and infrared cameras. Further, a lightweight segmenter sub-system called "Unet_xxs" is proposed, which can be used for iris semantic segmentation under restricted memory resources. The evaluations reveal that Unet_xxs reduces the number of parameters by 77% and duplicates the speed of state-of-the-art segmentation models with an EER drop smaller than 1% in Iris Recognition with 8.06 fps and IOU of 0.8382. INDEX TERMSEmbedding systems, Iris sensor, NIR Camera, Hardware.

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Section: B Iris Semantic Segmentationmentioning

confidence: 99%

Toward an Efficient Iris Recognition System on Embedded Devices

Benalcazar,

Tapia,

Vasquez

et al. 2023

IEEE Access

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“…The method proposed in [9] extracts reflections and occlusions by using a classifier that evaluates images transformed using the Rubber Sheet Model [14]. Other studies perform a semantic segmentation of the ocular region by using trained classifiers [10].…”

Section: B Segmentation Of the Reflection Regionsmentioning

confidence: 99%

“…However, the reflection regions are not always properly described and these iris segmentation methods do not compute a binary map representing only the reflections, which could be useful for a set of contexts, like photo editing applications. In the literature, there are also studies based on machine learning able to compute segmentation masks representing the reflections of the iris region [8]- [10]. However, they are not based on deep learning strategies.…”

Section: Introductionmentioning

confidence: 99%

Iris Reflection Segmentation from Ocular Images Acquired in Uncontrolled and Uncooperative Conditions

Labati,

Piuri,

Rundo

et al. 2023

2023 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applicati

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The segmentation of reflections from the iris region is a relevant task for biometric systems, human-machine interaction technologies, and photo editing applications. This task is particularly complex for ocular images acquired from uncooperative users in uncontrolled illumination and environmental conditions. Furthermore, to the best of our knowledge, all of the studies in the literature on methods specifically designed to detect reflections in the iris texture are based on algorithmic approaches. In this paper, we present the first study on deep neural networks for segmenting reflection regions from iris images. Specifically, we propose a modified version of the U-Net architecture based on an encoder (downsampler) characterized by a relatively low computational complexity, and designed with the aim of being applied on edge devices. Experiments have been performed for a dataset of 3,286 ocular images acquired from websites and social media in completely uncontrolled and uncooperative conditions. The obtained results prove that our proposed method can accurately segment the iris reflections for particularly challenging images. A detailed qualitative analysis also confirm the robustness of our method for non-ideal application contexts. Furthermore, experiments show that our method can increase the accuracy of state-of-the-art iris segmentation techniques based on deep neural networks.

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“…Osorio-Roig et al [127] implemented a semantic segmentation technique called HMRF-PyrSeg [156] which was basically designed to segment public images. e proposed technique was used to identify the iris region in the eye images captured within the visible wavelength spectrum.…”

Section: Other Segmentation Techniquesmentioning

confidence: 99%

“…e proposed technique was used to identify the iris region in the eye images captured within the visible wavelength spectrum. Also, Osorio-Roig et al [127] provided semantic information about various regions within the eye image, such as eyebrows, iris, sclera, and pupil to segment the region of the iris. To improve performance accuracy of this technique, future research may want to apply hybrid techniques that support combining regions of various segmentation levels and also to determine the most suitable segmentation level for each image automatically [127].…”

Section: Other Segmentation Techniquesmentioning

confidence: 99%

Iris Recognition Development Techniques: A Comprehensive Review

2021

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Recently, iris recognition techniques have achieved great performance in identification. Among authentication techniques, iris recognition systems have received attention very much due to their rich iris texture which gives robust standards for identifying individuals. Notwithstanding this, there are several challenges in unrestricted recognition environments. In this article, the researchers present the techniques used in different phases of the recognition system of the iris image. The researchers also reviewed the methods associated with each phase. The recognition system is divided into seven phases, namely, the acquisition phase in which the iris images are acquired, the preprocessing phase in which the quality of the iris image is improved, the segmentation phase in which the iris region is separated from the background of the image, the normalization phase in which the segmented iris region is shaped into a rectangle, the feature extraction phase in which the features of the iris region are extracted, the feature selection phase in which the unique features of the iris are selected using feature selection techniques, and finally the classification phase in which the iris images are classified. This article also explains the two approaches of iris recognition which are the traditional approach and the deep learning approach. In addition, the researchers discuss the advantages and disadvantages of previous techniques as well as the limitations and benefits of both the traditional and deep learning approaches of iris recognition. This study can be considered as an initial step towards a large-scale study about iris recognition.

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Semantic Segmentation of Color Eye Images for Improving Iris Segmentation

Cited by 7 publications

References 16 publications

Toward an Efficient Iris Recognition System on Embedded Devices

Toward an Efficient Iris Recognition System on Embedded Devices

Iris Reflection Segmentation from Ocular Images Acquired in Uncontrolled and Uncooperative Conditions

Iris Recognition Development Techniques: A Comprehensive Review

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